Cookbook • sgplot

This cookbook provides examples of the code used to produce various chart types using sgplot. There are also examples to demonstrate how to apply further customisation to sgplot charts.

If there is a chart type or task which you think would be useful to include here, please submit a suggestion.

Getting started

Required packages

The following packages are required to produce the example charts in this cookbook:

library(sgplot)
library(ggplot2)
library(dplyr)

# Use gapminder data for cookbook charts
library(gapminder)

Use of titles, subtitles and captions

Titles, subtitles and captions have been embedded in the charts in this cookbook for demonstration purposes. However, for accessibility reasons, it is usually preferable to provide titles in the body of the page rather than embedded within the image of the plot. For more guidance on the placement of chart titles, see the Analysis Function charts guidance.

use_sgplot

The examples in this cookbook use the sgplot theme and colour functions explicitly, however it may be easier to make use of the use_sgplot() function if your charts all require a similar style. More information on use_sgplot can be found on the sgplot homepage.

Line charts

Line chart with one line

gapminder |>
  filter(country == "United Kingdom") |>
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line(linewidth = 1, colour = sg_colour_values["dark-blue"]) +
  theme_sg() +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom 1952-2007",
    caption = "Source: Gapminder"
  )

A line chart using sgplot theme and dark blue colour.

Line chart with multiple lines

gapminder |>
  filter(country %in% c("United Kingdom", "China")) |>
  ggplot(aes(x = year, y = lifeExp, colour = country)) +
  geom_line(linewidth = 1) +
  theme_sg(legend = "bottom") +
  scale_colour_discrete_sg() +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
    caption = "Source: Gapminder",
    colour = NULL
  )

A multiple line chart using sgplot theme and main colour palette.

An example with line labels and no legend can be found in the Annotations section.

Bar charts

bar_data <-
  gapminder |>
  filter(year == 2007 & continent == "Americas") |>
  slice_max(order_by = pop, n = 5)

ggplot(bar_data, aes(x = reorder(country, -pop), y = pop)) +
  geom_col(fill = sg_colour_values["dark-blue"]) +
  theme_sg() +
  scale_y_continuous(
    labels = \(x) x / 1e6,
    limits = c(0, 3e8 * 1.02),
    expand = c(0, 0)
  ) +
  labs(
    x = NULL,
    y = NULL,
    title = "The United States is the most populous country \nin the Americas",
    subtitle = "Population of countries in the Americas (millions), 2007",
    caption = "Source: Gapminder"
  )

A bar chart using sgplot theme and dark blue colour.

A bar chart can sometimes look better with horizontal bars. This can also be a good option if your bar labels are long and difficult to display horizontally on the x-axis. To produce a horizontal bar chart, swap the variables defined for x and y in aes() and make a few tweaks to theme_sg(); draw grid lines for the x-axis only by setting the grid argument, and draw an axis line for the y-axis only by setting the axis argument.

ggplot(bar_data, aes(x = pop, y = reorder(country, pop))) +
  geom_col(fill = sg_colour_values["dark-blue"]) +
  theme_sg(grid = "x", axis = "y") +
  scale_x_continuous(
    labels = \(x) x / 1e6,
    limits = c(0, 3e8 * 1.02),
    expand = c(0, 0)
  ) +
  labs(
    x = NULL,
    y = NULL,
    title = "The United States is the most populous country \nin the Americas",
    subtitle = "Population of countries in the Americas (millions), 2007",
    caption = "Source: Gapminder"
  )

A horizontal bar chart using sgplot theme and dark blue colour.

Grouped bar chart

To create a grouped bar chart, set stat = "identity" and position = "dodge" in the call to geom_bar(). Also assign a variable to fill within aes() to determine what variable is used to create bars within groups. The legend argument in theme_sg() can be used to set the position of the legend.

grouped_bar_data <-
  gapminder |>
  filter(year %in% c(1967, 2007) &
           country %in% c("United Kingdom", "Ireland", "France", "Belgium"))

ggplot(grouped_bar_data,
       aes(x = country, y = lifeExp, fill = as.factor(year))) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_y_continuous(expand = c(0, 0)) +
  theme_sg(legend = "bottom") +
  scale_fill_discrete_sg() +
  labs(
    x = "Country",
    y = NULL,
    fill = NULL,
    title = "Living longer",
    subtitle = "Difference in life expectancy, 1967-2007",
    caption = "Source: Gapminder"
  )

A grouped bar chart using sgplot theme and colours from main palette

Stacked bar chart

To create a stacked bar chart, set stat = "identity and position = "fill" in the call to geom_bar() and assign a variable to fill as before. This will plot your data as part-to-whole. To plot counts, set position = "identity".

Caution should be taken when producing stacked bar charts. They can quickly become difficult to interpret if plotting non part-to-whole data, and/or if plotting more than two categories per stack. First and last categories in the stack will always be easier to compare across bars than those in the middle. Think carefully about the story you are trying to tell with your chart.

stacked_bar_data <-
  gapminder |>
  filter(year == 2007) |>
  mutate(lifeExpGrouped = cut(lifeExp,
                              breaks = c(0, 75, Inf),
                              labels = c("Under 75", "75+"))) |>
  group_by(continent, lifeExpGrouped) |>
  summarise(n_countries = n(), .groups = "drop")

ggplot(stacked_bar_data,
       aes(x = continent, y = n_countries, fill = lifeExpGrouped)) +
  geom_bar(stat = "identity", position = "fill") +
  theme_sg(legend = "right") +
  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
  coord_cartesian(clip = "off") +
  scale_fill_discrete_sg() +
  labs(
    x = NULL,
    y = NULL,
    fill = "Life Expectancy",
    title = "How life expectancy varies across continents",
    subtitle = "Percentage of countries by life expectancy band, 2007",
    caption = "Source: Gapminder"
  )

A stacked bar chart using sgplot theme and colours from main palette.

Histograms

gapminder |>
  filter(year == 2007) |>
  ggplot(aes(x = lifeExp)) +
  geom_histogram(binwidth = 5,
                 colour = "white",
                 fill = sg_colour_values["dark-blue"]) +
  theme_sg() +
  scale_y_continuous(expand = c(0, 0)) +
  labs(
    x = NULL,
    y = "Number of \ncountries",
    title = "How life expectancy varies",
    subtitle = "Distribution of life expectancy, 2007",
    caption = "Source: Gapminder"
  )

A histogram with sgplot theme and dark blue colour.

Scatterplots

gapminder |>
  filter(year == 2007) |>
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point(colour = sg_colour_values["dark-blue"]) +
  theme_sg(axis = "none", grid = "xy") +
  scale_x_continuous(
    labels = function(x) scales::dollar(x, prefix = "£")
  ) +
  scale_size_continuous(labels = scales::comma) +
  labs(
    x = "GDP",
    y = "Life\nExpectancy",
    title = stringr::str_wrap(
      "The relationship between GDP and Life Expectancy is complex", 40
    ),
    subtitle = "GDP and Life Expectancy for all countires, 2007",
    caption = "Source: Gapminder"
  )

A scatterplot using sgplot theme and dark blue colour.

Small multiples

gapminder |>
  filter(continent != "Oceania") |>
  group_by(continent, year) |>
  summarise(pop = sum(as.numeric(pop)), .groups = "drop") |>
  ggplot(aes(x = year, y = pop, fill = continent)) +
  geom_area() +
  theme_sg(axis = "none", ticks = "none", legend = "none") +
  scale_fill_discrete_sg() +
  facet_wrap(~ continent, ncol = 2) +
  scale_y_continuous(breaks = c(0, 2e9, 4e9),
                     labels = c(0, "2bn", "4bn")) +
  coord_cartesian(clip = "off") +
  theme(axis.text.x = element_blank()) +
  labs(
    x = NULL,
    y = NULL,
    title = "Asia's rapid growth",
    subtitle = "Population growth by continent, 1952-2007",
    caption = "Source: Gapminder"
  )

A small multiples area chart using sgplot theme and main colour palette.

Pie charts

stacked_bar_data |>
  filter(continent == "Europe") |>
  ggplot(aes(x = "", y = n_countries, fill = lifeExpGrouped)) +
  geom_col(colour = "white", position = "fill") +
  coord_polar(theta = "y") +
  theme_sg(grid = "none", axis = "none", ticks = "none") +
  theme(axis.text = element_blank()) +
  scale_fill_discrete_sg() +
  labs(
    x = NULL,
    y = NULL,
    fill = NULL,
    title = "How life expectancy varies in Europe",
    subtitle = "Percentage of countries by life expectancy band, 2007",
    caption = "Source: Gapminder"
  )

A pie chart using sgplot theme and main palette.

Focus charts

bar_data |>
  ggplot(
    aes(x = reorder(country, -pop), y = pop,
        fill = country == "Brazil")
  ) +
  geom_col() +
  theme_sg(legend = "none") +
  scale_y_continuous(
    labels = \(x) x / 1e6,
    limits = c(0, 3e8 * 1.02),
    expand = c(0, 0)
  ) +
  scale_fill_discrete_sg("focus", reverse = TRUE) +
  labs(
    x = NULL,
    y = NULL,
    title = "Brazil has the second highest population in the Americas",
    subtitle = "Population of countries in the Americas (millions), 2007",
    caption = "Source: Gapminder"
  )

A bar chart with the bar for Brazil highlighted in dark blue and other bars in grey.

Interactive charts

To make a ggplot2 chart interactive, use ggplotly() from the plotly package. Note however that ggplotly() has a number of ‘quirks’, including the following:

sgplot uses the ‘sans’ font family, however plotly does not recognise this font. To work around this you should add a further call to theme to set the font family for text to "".
Subtitles and captions are not supported in ggplotly(). As stated elsewhere in this guidance, titles and subtitles should ideally be included in the body of text surrounding a chart rather than embedded in the chart itself, and so this is hopefully not a big issue. This example therefore has no title, subtitle or caption.

p <-
  bar_data |>
  # Format text for tooltips
  mutate(tooltip = paste0(
    "Country: ", country, "\n",
    "Population: ", format(pop, big.mark = ",")
  )) |>
  ggplot(aes(x = reorder(country, -pop), y = pop, text = tooltip)) +
  geom_col(fill = sg_colour_values["dark-blue"]) +
  theme_sg(ticks = "x") +
  theme(text = element_text(family = "")) +
  scale_y_continuous(
    labels = \(x) x / 1e6,
    limits = c(0, 3e8 * 1.02),
    expand = c(0, 0)
  ) +
  labs(
    x = NULL,
    y = NULL
  )

plotly::ggplotly(p, tooltip = "text") |>
  plotly::config(
    modeBarButtons = list(list("resetViews")),
    displaylogo = FALSE
  )

sgplot currently only works with ggplot2 charts, however there are plans to develop the package further to support interactive Highcharts produced using the highcharter package.

Annotations

Labelling your chart is often preferable to using a legend, as often this relies on a user matching the legend to the data using colour alone. The legend can be removed from a chart by setting legend = "none" in theme_sg().

Either geom_label() or geom_text() can be used to annotate a chart. The main difference is that geom_label() draws a rectangle behind the text (white by default) and a border the same colour as the text (label.size = NA can be used to remove the border). geom_text() does not include a background and so text can be harder to read if it overlaps with other chart elements.

The easiest way to add an annotation is to manually define the co-ordinates of the required position.

ann_data <- gapminder |>
  filter(country %in% c("United Kingdom", "China"))

ann_data |>
  ggplot() +
  geom_line(aes(x = year, y = lifeExp, colour = country), linewidth = 1) +
  theme_sg(legend = "none") +
  scale_colour_discrete_sg() +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(limits = c(1952, 2017),
                     breaks = seq(1952, 2007, 5)) +
  geom_label(x = 2008, y = 73, label = "China",
             hjust = 0,
             vjust = 0.5,
             label.size = NA) +
  geom_label(x = 2008, y = 79.4, label = "United Kingdom",
             hjust = 0,
             vjust = 0.5,
             label.size = NA) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
    caption = "Source: Gapminder"
  )

A multiple line chart using sgplot theme and main2 palette with lines labelled.

However, this makes the code difficult to reuse as values are hard coded and not automatically generated from the data. Automating the position of annotations is possible, but more fiddly.

One way to automate this is to create a supplementary data frame with desired co-ordinates of the labels (see ann_labs in the example below). nudge arguments can be used to displace text to improve the positioning.

ann_labs <- ann_data |>
  group_by(country) |>
  filter(year == max(year)) |>
  ungroup()

ann_data |>
  ggplot() +
  geom_line(aes(x = year, y = lifeExp, colour = country), linewidth = 1) +
  theme_sg(legend = "none") +
  scale_colour_discrete_sg() +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(limits = c(1952, 2017),
                     breaks = seq(1952, 2007, 5)) +
  geom_label(data = ann_labs,
             aes(x = year, y = lifeExp, label = country),
             hjust = 0,
             vjust = 0.5,
             nudge_x = 0.5,
             label.size = NA) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
    caption = "Source: Gapminder"
  )

A multiple line chart using sgplot theme and main2 palette with lines labelled.

Annotations may also be used to add value labels to a bar chart. Note that geom_text() is used here as a background is not required.

ggplot(bar_data, aes(x = reorder(country, -pop), y = pop)) +
  geom_col(fill = sg_colour_values["dark-blue"]) +
  geom_text(aes(label = format(pop, big.mark = ",")),
            nudge_y = -max(bar_data$pop) * 0.05,
            colour = "white") +
  theme_sg() +
  scale_y_continuous(
    labels = \(x) x / 1e6,
    limits = c(0, 3e8 * 1.02),
    expand = c(0, 0)
  ) +
  labs(
    x = NULL,
    y = NULL,
    title = "The United States is the most populous country \nin the Americas",
    subtitle = "Population of countries in the Americas (millions), 2007",
    caption = "Source: Gapminder"
  )

A bar chart with white text labels at the end of each bar.

Other customisations

The following section demonstrates some common customisations made to ggplot2 charts. Each example builds on the previous one.

Note that theme_sg() has arguments to control the legend position and appearance of grid lines, axis lines and axis ticks. More information on accepted values can be found in the help file.

Customisations will be made to the following basic horizontal bar chart:

bar_data |>
  ggplot(aes(x = pop, y = country)) +
  geom_col(fill = sg_colour_values["dark-blue"]) +
  theme_sg(axis = "y", grid = "x")

A horizontal bar chart using sgplot theme and dark blue colour.

Sorting a bar chart

To control the order of bars in a chart, wrap the variable you want to arrange with reorder() and specify what variable you want to sort by. The following example sorts bars in ascending order of life expectancy. To sort in descending order, you would change this to reorder(country, desc(lifeExp)).

bar_data |>
  ggplot(aes(x = pop, y = reorder(country, pop))) +
  geom_col(fill = sg_colour_values["dark-blue"]) +
  theme_sg(axis = "y", grid = "x")

A horizontal bar chart using sgplot theme and dark blue colour with bars sorted in decreasing order by population.

Changing chart titles and wrapping text

Chart titles such as the main title, subtitle, caption, axis titles and legend titles, can be controlled using labs(). A title can be removed using NULL.

Remember that it is best practice to provide chart titles and subtitles in the main body of text, rather than embedded in the chart.

last_plot() +
  labs(
    x = NULL,
    y = NULL,
    title = "The United States is the most populous country in the Americas",
    subtitle = "Population of countries in the Americas (millions), 2007",
    caption = "Source: Gapminder"
  )

A bar chart with title, subtitle and caption. The axis titles have been removed. The main title is too long and the end is cut off.

If text is too long, it may be cut off or distort the dimensions of the chart. There are two suggested ways to solve this issue.

Insert \n within a string to force a line break:

last_plot() +
  labs(
    title = "The United States is the most populous country \nin the Americas"
  )

A bar chart with main title and y-axis title text wrapped onto two lines.

Use stringr::str_wrap() to set a maximum character width of the string:

last_plot() +
  labs(
    title = stringr::str_wrap(
      "The United States is the most populous country in the Americas",
      width = 50
    )
  )

A bar chart with main title and y-axis title text wrapped onto two lines.

Reducing space between chart and axis

By default, a bar chart will have a gap between the bottom of the bars and the axis. This can be removed as follows:

last_plot() +
  scale_x_continuous(expand = c(0, 0))

The equivalent adjustment can be made for the y-axis using scale_y_continuous.

Changing axis limits, breaks and labels

Axis limits, breaks and labels for continuous variables can be controlled using scale_x/y_continuous(). For discrete variables, labels can be changed using scale_x/y_discrete() or alternatively by recoding the variable in the data before creating a chart.

Note that further calls to scale_x/y_continuous will overwrite previous calls, hence why expand = c(0, 0) has been included again in these examples.

last_plot() +
  scale_x_continuous(expand = c(0, 0),
                     limits = c(0, 3e8 * 1.1),
                     breaks = seq(0, 3e8, 5e7),
                     labels = c(seq(0, 250, 50), "300 million"))

A bar chart with more x-axis breaks and formatted labels. The last label is suffixed with 'million'.

Labels can also be formatted using a function. The scales package provides many convenient functions for this, and can handle percentages, currency and thousands separators.

last_plot() +
  scale_x_continuous(expand = c(0, 0),
                     limits = c(0, 3e8 * 1.1),
                     breaks = seq(0, 3e8, 1e8),
                     labels = scales::label_comma())

A bar chart with fewer x-axis breaks and labels formatted with comma thousands separators.

Adjusting theme elements

If you find you need to adjust theme elements for your chart, this can be done using theme(). Note that this should be done after the call to theme_sg(), otherwise theme_sg() may overwrite the specifications you’ve made.

last_plot() +
  theme(axis.line.y = element_line(colour = "black"),
        axis.ticks.y = element_line(colour = "black"))

A bar chart using sgplot theme and dark blue colour, with y-axis lines and ticks coloured black.

Avoiding axis/grid lines being cut off

Axis lines and grid lines can sometimes appear ‘cut off’ if they are drawn at the limits of the chart range. You can see in the following example that the top grid line is slightly narrower than the adjacent tick mark on the y-axis. This is because the y-axis limit is 100%. As the grid line is centred at 100%, the top half of the line is ‘cut off’.

stacked_bar_data |>
  ggplot(aes(x = continent, y = n_countries, fill = lifeExpGrouped)) +
  geom_bar(stat = "identity", position = "fill") +
  theme_sg(legend = "bottom") +
  scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
  scale_fill_discrete_sg() +
  labs(
    x = NULL,
    y = NULL,
    fill = "Life Expectancy",
    title = "How life expectancy varies across continents",
    subtitle = "Percentage of countries by life expectancy band, 2007",
    caption = "Source: Gapminder"
  )

A stacked bar chart with top gridline is half the width of the adjoining tick mark and other grid lines.

This can be corrected as follows:

last_plot() +
  coord_cartesian(clip = "off")

A stacked bar chart with top gridline the same width as adjoining tick mark and other grid lines.

Adding a reference line

To add a horizontal or vertical line across the whole plot, use geom_hline() or geom_vline(). This can be useful to highlight a threshold or average level.

gapminder |>
  filter(country == "United Kingdom") |>
  ggplot(aes(x = year, y = lifeExp)) +
  geom_hline(yintercept = 75,
             colour = sg_colour_values["grey"],
             linewidth = 1,
             linetype = "longdash") +
  geom_line(linewidth = 1,
            colour = sg_colour_values["dark-blue"]) +
  annotate(geom = "text", x = 2007, y = 70, label = "Age 70") +
  theme_sg() +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom 1952-2007",
    caption = "Source: Gapminder"
  )

A line chart with dashed, grey horizontal line at age 75.

Using different colour palettes

sgplot provides colour palettes as set out by the Scottish Government Design System. These palettes have been developed to meet the Web Content Accessibility Guidelines 2.1 for graphical objects.

The main palette is the default for discrete colour/fill functions, and the sequential palette for continuous colour/fill functions.

The Scottish Government palettes are used by default, however palettes are also available for Social Security Scotland and the Government Analysis Function.

More information on the colours used in sgplot can be found at vignette("colours").

Using non-default sgplot colour palettes

There may be instances where you want to use an sgplot colour palette other than the default. The full list of available palettes can be found by running either sgplot::sg_colour_palettes or sgplot::af_colour_palettes.

To use the Scottish Government main-extended palette:

gapminder |>
  filter(country %in% c("United Kingdom", "China", "India",
                        "Sweden", "Namibia", "Brazil")) |>
  ggplot(aes(x = year, y = lifeExp, colour = country)) +
  geom_line(linewidth = 1) +
  theme_sg(legend = "bottom") +
  scale_colour_discrete_sg("main-extended") +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
    caption = "Source: Gapminder",
    colour = NULL
  )

A multiple line chart using sgplot theme and SG main-extended colour palette.

Note: This chart is for demonstration purposes only. Accessibility guidance recommends using a maximum of four colours to avoid clutter.

To use an Analysis Function palette, set palette_type = "af" when using any of the scale_ colour functions. For example, to use the Analysis Function main2 palette:

gapminder |>
  filter(country %in% c("United Kingdom", "China")) |>
  ggplot(aes(x = year, y = lifeExp, colour = country)) +
  geom_line(linewidth = 1) +
  theme_sg(legend = "bottom") +
  scale_colour_discrete_sg("main2", palette_type = "af") +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
    caption = "Source: Gapminder",
    colour = NULL
  )

A multiple line chart using sgplot theme and AF main colour palette.

Using your own colour palette

There may be instances where you’d like to use a colour palette that is not available in sgplot. If so, this should be carefully considered to ensure it meets accessibility requirements. The Analysis Function guidance outlines appropriate steps for choosing your own accessible colour palette and should be used.

my_palette <- c("#0F820D", "#000000")

gapminder |>
  filter(country == "United Kingdom") |>
  ggplot(aes(x = year, y = lifeExp)) +
  geom_line(linewidth = 1, colour = my_palette[1]) +
  theme_sg() +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom 1952-2007",
    caption = "Source: Gapminder"
  )

A line chart using sgplot theme and first colour from custom palette.

gapminder |>
  filter(country %in% c("United Kingdom", "China")) |>
  ggplot(aes(x = year, y = lifeExp, colour = country)) +
  geom_line(linewidth = 1) +
  theme_sg(legend = "bottom") +
  scale_colour_manual(values = my_palette) +
  scale_y_continuous(limits = c(0, 82),
                     breaks = seq(0, 80, 20),
                     expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(1952, 2007, 5)) +
  labs(
    x = "Year",
    y = NULL,
    title = "Living Longer",
    subtitle = "Life Expectancy in the United Kingdom and China 1952-2007",
    caption = "Source: Gapminder",
    colour = NULL
  )

A multiple line chart using sgplot theme and colours from custom palette.

Adding a new colour palette to sgplot

If you use a different palette regularly and feel it would be useful for this to be added to sgplot, please make a suggestion as per the contributing guidance.

Acknowledgments

This cookbook and the examples it contains have been inspired by the BBC Visual and Data Journalism cookbook for R graphics and their bbplot package.